For traditional face recognition methods,the process of manual design features is complex and face recognition rate is low. The feature discrimination ability of general deep learning classification model is weak, for the open set face recognition. Aiming at these two problems, a kind of deep convolution neural network is proposed, which combines the classification loss with the central loss as the model training monitoring signal. Firstly, a deep face recognition model based on the initialization parameters obtained from the public dataset is fine tuned using application scene dataset,which can effectively solve the problem of training data is too small and data distribution differences and improve the training speed of the model. Then, the traditional loss function and the new central loss are used as the monitoring signals in the process of transfer learning, which can make the intra-class aggregation and inter-class dispension and improve the discriminative ability of the model output features. Finally, the principal component analysis is used to remove the redundant face features, reduce the complexity and improve face recognition rate. The experimental results show that our algorithm can automatically extract features compared with the traditional face recognition algorithm and relative to the general deep learning classification model, the algorithm makes the feature representation more discriminative with metic learning. A higher recognition rate has been achieved in the self built test set and the LFW and YouTube Faces Standard test sets.